| Summary: | 碩士 === 國立屏東科技大學 === 機械工程系 === 87 === In this study, a predictive model was built to predict the milling forces and the surface roughness for milling S45C carbon steel. Then, the optimum milling parameters were found under the constraint of surface roughness and meet the maximum metal removal rate.Artificial neural network theory was used to build the predictive model. The controlling parameters are milling speed, feed and depth of milling, and the predicted machining parameters of face milling are X, Y and Z direction of milling forces and the surface roughness of workpiece. The errors of milling forces are 7.35% on X direction, 5.73% on Y direction and 4.3% on Z direction, respectively. The error of surface roughness is 4.6%.In addition, the genetic algorithm was used in the optimum model to find the optimum milling parameters. It is found that the milling feed and depth of milling are adjusted to close the upper boundary of the selected controlling parameters, and the milling speed is close to satisfy the constraint of surface roughness such that the maximum metal removal rate is obtained.
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